Detailed wrinkle generation of virtual garments from a single image
Pages 4053 - 4071
Abstract
The presence of proper wrinkles is important while modeling realistic virtual garments. Unlike previously used full 3D information methods, our approach achieves detailed garment generation from a single image. First, we retrieve a garment image similar to the initial virtual garment based on content-based image retrieval (CBIR) method. Then, we preprocess the image with a combination of human body reshaping, image segmentation and shape recovery, to obtain the 3D wrinkle details. Finally, the garment height are synthesized into the virtual garment. For better suit the posture of the human body, excess garment energy are released to remove the unmatched wrinkles. We apply our method to various styles of virtual garments, and it enable virtual characters in general pose to be dressed in these garments and complete wrinkle generation. Compared with existing garment modeling methods, the experimental results show that the proposed method could quickly capture the realistic wrinkles of virtual garments with less manual operation and achieve more realistic wrinkles for virtual garments.
References
[1]
Abdel-Hakim AE and Farag AA CSIFT: a SIFT descriptor with color invariant characteristics Comput Vision Pattern Recog 2006 2 1978-1983
[2]
Aguiar ED, Sigal L, Treuille A, et al. Stable Spaces for Real-Time Clothing ACM Trans Graphics 2010 29 4 106:1-106:9
[3]
Berthouzoz F, Garg A, Kaufman DM, et al. Parsing sewing patterns into 3d garments ACM Trans Graph 2013 32 4 1-12
[4]
Bradley D, Popa T, Sheffer A, et al. Markerless garment capture ACM Trans Graph 2008 27 3 1-9
[5]
W. Chen, B. Zhou, F. X. Lu et. al., “Garment modeling with a depth camera,” ACM Trans Graph, vol. 34, no. 6, pp. 1–12, 2015.
[6]
Daněřek R, Dibra E, Öztireli C, et al. DeepGarment: 3d garment shape estimation from a single image Comput Graphics Forum 2017 36 2 269-280
[7]
A. Gordo, J. Almazan, J. Revaud et. al., “Deep image retrieval: learning global representations for image search,” Eur Conf Comp Vision, pp. 241–257, 2016.
[8]
Guo JM, Li JM (2015) CNN based hashing for image retrieval. Computer Vision & Pattern Recognition
[9]
H. Jegou, M. Douze, C. Schmid et. al., “Aggregating local descriptors into a compact image representation,” Comput Vision Pattern Recog, vol. 238, no. 6, pp. 3304–3311, 2010.
[10]
Li Y, Sun J, Tang CK, et al. Lazy Snapping ACM Trans Graph 2004 23 3 303-308
[11]
Liu ZQ, Wang SJ, and Tian Q Fine-Residual Vlad for Image Retrieval Neurocomputing 2016 173 P3 1183-1191
[12]
Meng YW, Mok PY, and Jin XG Computer aided clothing pattern design with 3d editing and pattern alteration Computer Aided Design 2012 44 8 721-734
[13]
Perronnin F, Sanchez J, and Mensink T Improving the fisher kernel for large-scale image classification Eur Conf Comput Vision 2010 115 7 143-156
[14]
Popa T, Zhou Q, Bradley D, et al. Wrinkling captured garments using space-time data-driven deformation Eurographics 2009 28 2 427-436
[15]
Robson C, Maharik R, Sheffer A, et al. Context-Aware Garment Modeling from Sketches Comput Graph 2011 35 3 604-613
[16]
D. Rohmer, T. Popa, M. P. Cani et. al., “Animation wrinkling: augmenting coarse cloth simulations with realistic-looking wrinkles,” ACM Trans Graph, vol. 29, no. 6, pp. 1–8, 2010.
[17]
Seddati O, Dupont S, Mahmoudi S et al (2017) Towards good practices for image retrieval based on CNN features. IEEE Int Conf Comput Vision Workshop:1246–1255
[18]
Simonyan K, Zisserman A (2015) “Very deep convolutional networks for large-scale image recognition,” In ICLR.
[19]
Vlasic D, Peers P, Baran L, et al. Dynamic shape capture using multi-view photometric stereo ACM Trans Graph 2009 28 5 1-11
[20]
Wang HM Ruel-Free Sewing Pattern Adjustment with Precision and Efficiency ACM Trans Graphics 2018 37 4 53:1-53:13
[21]
Wang HM, Hecht F, Ramamoorthi R, et al. Example-Based Wrinkle Synthesis for Clothing Animation ACM Trans Graphics 2010 29 4 107:1-107:8
[22]
Yang S, Pan ZR, Amert TY, et al. Physics-inspired garment recovery from a single-view image ACM Trans Graph 2018 37 5 1-14
[23]
Yu W, Yang KY, Yao HX, et al. Exploiting the complementary strengths of multi-layer CNN features for image retrieval Neurocomputing 2016 237 235-241
[24]
Zhang MM, Lin L, Pan ZG, et al. Topology-independent 3D garment fitting for virtual clothing Multimedia Tools Appl 2015 74 9 3137-3153
[25]
Zheng L, Yang Y, and Tian Q SIFT Meets CNN: A Decade Survey of Instance Retrieval IEEE Trans Pattern Anal Mach Intell 2018 40 5 1224-1244
[26]
Zhou SZ, Fu HB, Liu LG, et al. Parametric Reshaping of Human Bodies in Images ACM Trans Graphics 2010 29 4 126:1-126:10
[27]
Zhou B, Chen XW, Fu Q, et al. Garment modeling from a single image Comput Graphics Forum 2013 32 7 85-91
[28]
Y. X. Zhu, Y. J. Peng, A. Boodaghian Asl (2019) “Dual Adaptive Adjustement for Customized Garment Pattern”. Sci Prog. vol. 2019, Article ID 8069373, 12 pages
[29]
Zurdo JS, Brito JP, and Otaduy MA Animating wrinkles by example on non-skinned cloth IEEE Trans Vis Comp Graphics 2012 19 1 149-158
Index Terms
- Detailed wrinkle generation of virtual garments from a single image
Index terms have been assigned to the content through auto-classification.
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© Springer Science+Business Media, LLC, part of Springer Nature 2020.
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Kluwer Academic Publishers
United States
Publication History
Published: 01 January 2021
Accepted: 16 September 2020
Revision received: 16 July 2020
Received: 06 July 2019
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- Research-article
Funding Sources
- the National Key Research and Development Program of China
- the Natural Science Foundation of Shandong Province
- the National Natural Science Foundation of China
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